Javier M.J. Ruiz-Velazcoa,b, Alfredo Hernández-Llamasc,⁎, Victor M. Gomez-Muñozd, Francisco J. Magallonc a
Departamento de Desarrollo de Tecnologías, Centro Interdisciplinario de Ciencias Marinas del IPN, Av. Instituto Politécnico Nacional s/n, Col. Playa Palo de Santa Rita, La Paz, B.C.S 23096, Mexico
b
Dirección de Fortalecimiento de la Investigación, Universidad Autónoma de Nayarit, Cd. de La Cultura Amado Nervo s/n Tepic, Nayarit 63255, Mexico
c
Programa de Acuicultura, Centro de Investigaciones Biológicas del Noroeste (CIBNOR). Mar Bermejo 195, Col. Playa Palo de Sta. Rita, La Paz, B.C.S. 23096, Mexico
d
Departamento de Pesquerías y Biología Marina, Centro Interdisciplinario de Ciencias Marinas del IPN, Av. Instituto Politécnico Nacional s/n, Col. Playa Palo de Santa Rita, La Paz, B.C.S. 23096, Mexico
a b s t r a c t a r t i c l e i n f o
Article history: Received 10 May 2009
Received in revised form 22 December 2009 Accepted 22 December 2009
Keywords: Production dynamics Litopenaeus vannamei White spot disease (WSSV)
A dynamic stock model was used to predict biomass of shrimp Litopenaeus vannamei when affected by white spot disease. A database prepared from records of intensive commercial farms in Mexico was used for estimating model parameters for summer and winter production cycles. Parameters were analyzed in relation to stocking density, pond size, and mean values of water quality variables measured during the cycles. Significant results from correlation analysis indicated that final weight of shrimp was positively correlated with mean pond water temperature and dissolved oxygen, but inversely correlated with salinity. When temperature and oxygen increased or salinity decreased, mortality from the disease diminished. Early mortality occurred when water temperature increased, oxygen decreased, or large ponds were used. Stocking density did not affect production parameters. Simple linear regression showed that differences in management of aeration affected oxygen levels. Oxygen concentration and aeration were important factors determining the magnitude of mortality from disease and the time when it occurred. Diminished mortality occurred later in the culture period with higher aeration or early start of aeration. Multiple regression analysis was used to predict model parameters as a function of water quality and management variables. Simple regression analysis and an equivalence test indicated that biomass at harvest was adequately predicted by the stock model and multiple regression coefficients. Predicting shrimp production indicated that raising aeration from 9000 to 14 000 horsepower per hour per hectare (Hp h ha− 1) increased biomass at harvest from 6610 to 8750 kg ha− 1(32%). On the other hand, starting aeration at the beginning of the culture cycle resulted in 8360 kg ha− 1, while starting after 5 weeks yielded 6840 kg ha− 1representing a reduction of 18%. Management of aeration in small ponds is recommended as an approach to reducing mortality from white spot disease.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
Shrimp farming is the most important aquaculture activity in Mexico. According toCONAPESCA (2007), production during 2007 reached 112 000 tons, representing 60% of the total aquaculture production.
The effect of salinity on the immune response and outbreaks of white spot disease in Marsupenaeus japonicus were studied byYu and Guan (2003).Liu et al. (2006)studied the effect of acute change in salinity on the white spot syndrome in Fenneropenaeus chinensis.
Vidal et al. (2001) and Rahman et al. (2006)studied the effects of hyperthermia on the incidence of WSSV in juvenile L. vannamei. Other Aquaculture 300 (2010) 113–119
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Aquaculture
dynamic perspective has not been addressed. In this investigation, the dynamics of intensive production of L. vannamei is studied by quantitatively analyzing the relationships between growth and mortality parameters and water quality and management variables.
For this investigation, databases from farms in the State of Nayarit were used. State of Nayarit is the fourth larger producer in Mexico after Sonora, Sinaloa, and Baja California Sur (CONAPESCA, 2007). Serious impact and mortality from the WSSV have occurred in Nayarit since 1999 and production losses, as high as 40%, has been estimated (Martínez-Ramírez at Comité Estatal de Sanidad Acuícola del estado de Nayarit, 2007, pers. comm.).
2. Materials and methods 2.1. Data survey
Two of the three intensive farms in Nayarit were selected for their more complete databases and correct diagnosis of the white spot disease. The study units were ponds ranging from 0.8 to 8 ha that were in use during the study period and where the disease was diagnosed: 19 during summer 2004, 18 during summer 2005, and 12 during winter 2006. For each pond (case), the following variables were analyzed: shrimp growth, survival,final biomass yield, initial stocking density, pond size, aeration rates, water temperature, salinity, and dissolved oxygen.
Aeration rates were recorded daily, and dissolved oxygen and temperature were measured twice daily (06:00 and 18:00 h) using oxymeters (Model 55, YSI, Yellow Springs, OH). Salinity was measured weekly using refractometers (Aquafauna Bio-Marine, Hawthorne, CA). Weight of shrimp was estimated on a weekly basis using 0.01 g and 0.1 g precision balances (Ohaus Corp., Pine Brook, NJ). Survival was estimated by evaluating shrimp populations using 1.5 m radius cast nets which, depending on the size of shrimp, were made of 3.2 or 25.4 mm square mesh knotted monofilament line.
Monitoring for the white spot disease was on a monthly basis using a PCR diagnostic kit (IQ2000, Farming IntelliGene Tech Corp., Taiwan). When symptoms of the disease were observed, additional PCR tests were performed, and the results were confirmed from samples processed by a certified laboratory using the same diagnostic kit and a similar kit (DiagXotics, Wilton, CT). After the diagnosis was confirmed, monitoring was conducted weekly. Mortality caused by the disease was calculated as the difference between shrimp population before and after the disease occurred.
2.2. Stock model
A stock model was used to predict shrimp biomass per hectare at time t (bt):
bt= wtnt; ð1Þ
where wtis the weight of shrimp and ntis the number of surviving
where ntis the number of survivors (%), n0is the initial population, Z1
is the instantaneous mortality rate previous to the time when die-off from disease occurred (tw), m is mortality from disease (%) (hereafter,
mortality), and Z2 is the instantaneous mortality rate after tw.
Nonlinear regression algorithms available in Statistica 6.0 (StatSoft, Tulsa, OK) were used to fit the growth and survival equations; significance was set at Pb0.05 for regression ANOVA.
2.3. Correlation and linear regression analyses
A matrix was prepared to analyze correlations between para- meters of the stock model and water quality and management variables. The mean values of water quality variables measured during the cultivation periods were analyzed. The relationship between m and mean dissolved oxygen (hereafter, oxygen) was analyzed, using simple linear regression. The relationship was established using either, mean oxygen for the complete cultivation period or the portion of the cultivation period previous to tw. The same kind of relationship
was established between tw and oxygen. To determine whether
aeration management in summer 2004 differed from summer 2005, the t-test was used to define differences in mean water temperature, oxygen, and total aeration and the time when aeration started. The product of horsepower by hours of aeration per hectare (Hp h ha− 1) was used to measure total aeration (hereafter, aeration).
After significant differences in management were found between the two summer seasons, the following relationships were defined: oxygen-aeration, oxygen-starting time of aeration, m-aeration, tw-
aeration, m-starting time of aeration, and lag in tw after aeration
started-starting time of aeration. For the last relationship, 88% of the ponds was used, where die-offs occurred after aeration started. This percentage of ponds was significantly higher than 50% (Pb0.001), which would be the situation if die-offs occurred randomly before or after aeration started. Correlation analysis, simple linear regression, and t-tests were performed using STATISTICA 6.0. Significance was set at Pb0.05.
Multiple linear regression analyses were carried out to predict values of growth and mortality parameters as a function of water quality and management variables. Accordingly:
Qt = a0+ a1T + a2S + a3O + a4D + a5PS; ð4Þ
where Q is either wf, k, Z1, tw, m, or Z2, from the stock model, T is
pond water temperature, S is salinity, O is oxygen, D is initial stocking density, and PS is pond size.
The values of water quality variables measured for all the cases were analyzed, and the backward’ stepwise regression procedure in Stata 10 (StataCorp, College Station, TX) was used with Pb0.05 to accept or reject predictors. This regression procedure deals automat- ically with collinearity according to the methods described inRencher (2002).
The predictive capability of the stock model, using the coefficients estimated from the multiple regression analyses, was tested by the
3. Results
In terms of regression ANOVA, significant results were obtained when growth and survival curves were fitted to the data sets corresponding to all the cases. The growth model showedflexibility to describe the different types of curves observed in the database (Fig. 1a). Survival curves indicated that mortality from disease occurred once and abruptly, covering 1 to 2 weeks, during the cultivation period. The survival model adequately described this mortality when it occurred early, midway, or late in the grow-out cycle (Fig. 1b).
Significant results from correlation analysis showed that, depend- ing on the season,final weight of shrimp was positively correlated with high pond water temperature and oxygen, but inversely correlated with salinity (Table 1).
Higher values of the instantaneous mortality rates (Z1 and Z2)
occurred when temperatures increased during the summer or when large ponds were used, but decreased when oxygen levels were high (Table 1). Mortality from WSSV infection was lower when temper- ature and oxygen increased, or when salinity declined. Early mortality was observed when water temperature increased or when large
ponds were used. There were no significant correlations for parameters k or for any of the production parameters and culture density.
Simple linear regression showed that the time of mortality and percentage mortality were directly and inversely related to oxygen, respectively (Table 2). Similar results were observed when mean oxygen values previous to the occurrence of mortality were used in the analysis (Table 2). This was direct evidence of the importance of high oxygen levels for reducing the impact of WSSV infection.
Water temperature and salinity did not differ significantly during the 2004 and 2005 summers, although significant differences existed in initial stocking density, aeration intensity, the time when aeration started, and oxygen levels (Table 3). During 2004, aeration was more intensive and started within thefirst three weeks of cultivation. In 2005, some ponds did not use aeration until the 14th week, which resulted in lower mean values of aeration and later starting time of aeration. Differences in aeration management resulted in higher oxygen levels in 2004 and lower levels in 2005 (Table 3). Simple linear regression showed that oxygen was directly related to total aeration, while inverse relationships were observed for each summer between oxygen and the time when aeration started (Table 4).
Simple linear regression indicated that the time of mortality and percentage mortality were positively and negatively related to aeration, respectively (Table 5). On the other hand, a direct relationship occurred between mortality and starting time of aeration (Table 5). Also, the lag between start of aeration and the time when mortality occurred was inversely related to starting time of aeration (Table 5). These results indicate that early aeration and higher total aeration diminish mortality from WSSV.
Results from multiple regression analyzes were, in general, consistent with correlation analysis (Table 6). Higherfinal weight of Table 1
Correlation matrix of parameters of the stock model and water quality and management variables. Correlation coefficients that were not significant are omitted.
Parameter Temperature Salinity Dissolved oxygen Pond size
Summer Winter Summer Summer Winter Summer
wf 0.53 −0.51 0.73 Z1 0.34 −0.73 0.34 tw −0.35 0.73 −0.37 M −0.65 0.36 −0.54 −0.49 Z2 0.43 0.26 −0.61 0.50 Table 2
Relationships of the time when mortality from disease occurred (tw) and percentage
mortality (m) with mean dissolved oxygen (O).
Equation P Season tw= 4.44 O−26.3 0.0001 Summer tw= 3.50 Owa−17.8 0.0001 Winter m =−4.00 O+46.6 0.0001 Summer m =−2.67 Ow+ 34.9 0.008 Summer m =−6.88 O+126.8 0.043 Winter a
Ow= mean oxygen values previous to mortality from disease.
115 J.M.J. Ruiz-Velazco et al. / Aquaculture 300 (2010) 113–119
shrimp was predicted for increased water temperature and lower salinity. Decreases in the instantaneous mortality rates and mortality from WSSV were associated with increases in temperature and oxygen. Low mortality was also predicted for low salinity. Delays in mortality were predicted when temperature and oxygen were high. Consistent with correlation analysis, no significant results were obtained involving parameter k or cultivation density. Pond size, in contrast, was not considered necessary as a predictor.
A significant relationship was obtained between biomass yields in the database and the yields predicted by the stock model, using the regression coefficients listed inTable 6(Fig. 2). The regression
coefficient did not differ significantly from 1 and significant equivalence between the coefficient and 1 was determined. Residual analysis did not indicate directional deviations from thefitted straight line and an adequate predictive capability of the model was achieved. The dynamics of production predicted with the stock model showed that, for summer, reductions in disease mortalities and delays in the occurrence of mortalities were associated with increasing levels of aeration (Fig. 3a). Increases from 9000 to 14 000 Hp h ha− 1 corresponded to an increase in biomass at harvest from 6610 to 8750 kg ha− 1, representing an additional 32% in shrimp production. Similar results were obtained when production was predicted as a function of the starting time of aeration (Fig. 3b). When compared with yields obtained when aeration was initiated at the start of the grow-out cycle, delayed aeration, starting as late as 5 weeks, corresponded to a reduction of yields from 8360 to 6840 kg ha− 1 and represented a loss of 18% in shrimp biomass. During winter, using 700 Hp h ha− 1, rather than 300 Hp h ha− 1, resulted in a 990% increase in biomass (Fig. 4a), while starting aeration at 5 weeks rather than 3 weeks after the start of the grow-out, reduced biomass by 92% (Fig. 4b).
Table 4
Relationships between dissolved oxygen (O) and aeration (A) and starting time of aeration (S).
Equation P Season (year)
O = 0.0002 A + 6.15 0.02 Summer
O =−0.232 S+9.62 0.039 Summer (2004)
O =−0.099 S+8.05 0.009 Summer (2005)
Table 5
Relationships of the time of mortality from disease (tw), percentage mortality (m), and
lag between the start of aeration and when mortality occurred (L) with total aeration (A) and starting time of aeration (S).
Equation P Season (year)
tw= 0.0012 A + 2.84 0.005 Summer (2004) tw= 0.0005 A−0.3352 0.043 Summer (2005) tw= 0.0013 A + 4.58 0.052 Winter (2006) m =−0.0011 A+24.66 0.039 Summer m = 0.9949 S + 10.72 0.042 Summer (2005) L =−3.0952 S+16.90 0.02 Summer L =−0.72 S+4.22 0.0001 Winter Table 6
Regression coefficients used to predict parameter values of the stock model as a function of water quality variables and density.
Parameter Temperature Salinity Dissolved oxygen Density Intercept
wf 2.8044 −0.7941 −41.5986
Z1 −0.0027 −0.0109 −0.0006 0.2382
tw 1.8603 3.7632 −79.5836
M −8.5830 0.9929 −4.4808 296.5821
Z2 −0.0085 0.1014
4. Discussion
The proposed stock model adequately described and predicted the dynamics of intensive production of L. vannamei when infected by the WSSV. This was a consequence of the adequacy of the growth and survival models used, as well as the statistical models derived from using multiple regression analysis. These regression models are considered predictive rather than explanatory tools, yet the predictors used for the models were in acceptable agreement with the relation- ships observed using correlation analysis between parameters of the
prevalent in Ecuadorian populations of L. vannamei during the winter (Rodríguez et al., 2003). For Marsupenaeus japonicus, Guan et al. (2003)found significantly lower concentrations of WSSV at 33 °C, compared to concentrations at 23 and 28 °C. We found, that for winter season, results between temperature and prevalence of the WSSV were similar to most reports in the literature. However, in summer, the effect of temperature was apparently opposite to what could be expected in terms of mortality rates and the time of mortality caused by the disease. We attribute this to the mean temperature in 2005 being slightly higher than in 2004, although adverse conditions prevailed in 2005 from lower aeration and dissolved oxygen (Table 3). There is very little understanding of the effect of salinity on outbreaks of white spot disease.Liu et al. (2006)reported that acute reductions in salinity, from 22 to 14 ppt in 1 h, increased the WSSV load in Fennerepenaeus chinensis. We found lower average salinity was related to lower mortality. In the study ofLiu et al. (2006), the precipitous reduction in salinity acted as a stressor, a different set of conditions than in our investigation, where levels of salinity were average values of individual ponds with independent shrimp popula- tions, which explains the overall effect of salinity during the grow- out period. Salinities lower than marine water (15–30 ppt) were recommended for cultivating L. vannamei (Hernandez-Llamas and Villarreal-Colmenares, 1999). The results from the present study showed that lower salinity promoted shrimp growth, supporting the explanation that better general cultivating conditions prevailed in ponds with lower salinity levels.
High stocking density is considered a risk factor for outbreaks of shrimp disease because it increases the number of contacts (Kautsky et al., 2000); however, no association between stocking density of P. monodon and WSSV infection was observed byCorsin et al. (2001). They attribute this to problems with sampling shrimp populations that are not uniformly distributed in ponds. Neither did we find evidence to support a relationship between stocking density and mortality or the time of the die-offs.
No relationship was found between shrimp growth and stocking density.Sandifer (1991)analyzedfinal size of L. vannamei for stocking densities ranging from 20 to 200 postlarvae m− 2and did notfind a tendency of shrimp size to decline as stocking density increased. In a survey of 23 extensive semi-intensive farms and intensive farms,
Hopkins and Villalon (1992)found that the relationship betweenfinal shrimp size and stocking density was barely discernible. The quality of postlarvae appears to be more influential than stocking density on the final size of shrimp (Sandifer, 1991). Successful intensive production of L. vannamei requires skilled farming practices. These practices may be another factor explaining the absence of an effect related to stocking density.
Low oxygen levels in ponds were found to reduce immune defense in L. stylirostris and P. monodon and increase susceptibility to infectious diseases (Le Moullac et al., 1998). We found that oxygen and aeration were major factors in the dynamics of intensive production of L. vannamei. High concentrations of oxygen led to larger harvested shrimp and increased biomass by reducing mortality from WSSV. From our database, high oxygen and aeration may be seen as a management strategy of farmers in response to high shrimp Fig. 4. Predicted dynamics of shrimp production as a function of total aeration (a) and
start of aeration (b) during winter.
117 J.M.J. Ruiz-Velazco et al. / Aquaculture 300 (2010) 113–119
association between acid-smelling pond bottom and presence of the white spot disease in P. monodon (Corsin et al., 2001). Artificial aeration results in increased pond oxygen, water circulation, and pond bottom removal, creating eroded and sedimentation areas (Boyd, 1998). Early start of aeration may contribute to the prevention of accumulating organic matter in the bottoms of shrimp ponds. Late start of aeration may cause removal of accumulated organic matter, with a harmful effect on shrimp.
Rameshthangam and Ramasamy (2006) and Mohankumar and Ramasamy (2006)working respectively with Penaeus monodon and Fenneropenaeus indicus found that shrimp infected with WSSV showed oxidative stress, as indicated by increased lipid peroxidation in tissues (including the gills), and depressed activity of antioxidant enzymes. According to these authors, lipid peroxidation occurs as a consequence of pro-oxidants liberated when ferrous radicals (Fe2+)
are transformed to ferric radicals (Fe3+). It is known that removing
pond sediment increases ferrous radicals in the water column (Boyd, 1990). It is likely that suspension of pond bottom sediments by aeration increases oxidation of these radicals to ferric oxide and subsequently leads to oxidative stress in shrimp. Depressed respira- tory activity in infected shrimp will lead to greater vulnerability when dissolved oxygen is low. Still, further research could confirm whether stirring of bottom sediments increases oxidative stress and reduces respiratory capacity in L. vannamei.
The influence of intraday variation in oxygen and temperature on the onset of the white spot disease was studied in semi-intensive farming of L. vannamei in Mexico (CONACyT, 2007). Short periods of cloudy days result in oxygen levels and temperature during evening